High Performance Computing Ontology

FLOW ALGEBRA
High Performance Computing Ontology
Kireth Semantic Research Archive

PAGE:
https://kirethresearch.org/booksai-core/ontologies/computing-ontologies/high-performance-computing/high-performance-computing-ontology-3.html

AUTHORS:
Mark Anthony Higgins — Sky (Recursive AI)

FLOW "HighPerformanceComputingOntology" v4.0

# ---------- DOMAIN ----------
DOMAIN := ComputingOntology
SUBDOMAIN := HighPerformanceComputing
ONTOLOGY := HighPerformanceComputingOntology

# ---------- PRIMARY ENTITY ----------
ENTITY HighPerformanceComputing {
    definition:
        "A computing discipline concerned with architectures, algorithms,
         parallel execution methods and resource-management techniques used
         to perform computationally intensive workloads at very high speed,
         scale or throughput.";

    objectives: [
        Performance,
        Parallelism,
        Scalability,
        Throughput,
        Efficiency,
        LargeScaleComputation
    ];
}

# ---------- CORE ENTITIES ----------
HPCSystem := {
    ComputeNode,
    Processor,
    Accelerator,
    Memory,
    Interconnect,
    Storage,
    Scheduler
};

HPCResource := {
    CPU,
    GPU,
    Accelerator,
    Memory,
    NetworkBandwidth,
    StorageBandwidth
};

HPCWorkload := {
    Job,
    ParallelApplication,
    Simulation,
    NumericalModel,
    ScientificApplication,
    DataIntensiveApplication
};

ExecutionModel := {
    SerialExecution,
    SharedMemoryParallelism,
    DistributedMemoryParallelism,
    HybridParallelism,
    AcceleratorParallelism
};

# ---------- FUNDAMENTAL RELATIONSHIPS ----------
HPCSystem CONSISTS_OF ComputeNode;
ComputeNode CONTAINS Processor;
ComputeNode MAY_CONTAIN Accelerator;
ComputeNode HAS Memory;
ComputeNode CONNECTS_THROUGH Interconnect;
HPCSystem USES Storage;
Scheduler ALLOCATES HPCResource TO HPCWorkload;
HPCWorkload EXECUTES_ON HPCSystem;

# ---------- PARALLEL DECOMPOSITION ----------
Problem ->
    AnalyseDependencies ->
    Decompose ->
    ParallelTasks
    -> MapToResources
    -> Execute
    -> Synchronise
    -> AggregateResult;

Decomposition := {
    DomainDecomposition,
    FunctionalDecomposition,
    DataDecomposition,
    TaskDecomposition
};

IF TasksIndependent
THEN AvailableParallelism := INCREASE;

# ---------- PARALLELISM ----------
Parallelism := {
    InstructionLevelParallelism,
    DataParallelism,
    TaskParallelism,
    ThreadParallelism,
    ProcessParallelism
};

DataParallelism APPLIES SameOrRelatedOperation TO DataPartitions;
TaskParallelism EXECUTES DistinctTasks Concurrently;

# ---------- SHARED MEMORY ----------
SharedMemoryModel :=
    MultipleThreads
    -> SharedAddressSpace;

Thread READS_OR_WRITES SharedMemory;

Synchronisation MAY_BE_REQUIRED_FOR ConflictingAccess;

SharedMemoryControl := {
    Lock,
    Barrier,
    AtomicOperation,
    CriticalSection
};

# ---------- DISTRIBUTED MEMORY ----------
DistributedMemoryModel :=
    MultipleProcesses
    + SeparateMemorySpaces
    + MessagePassing;

Process SENDS Message;
Process RECEIVES Message;

MessagePassing COORDINATES DistributedProcesses;

# ---------- HYBRID MODEL ----------
HybridParallelism :=
    DistributedMemoryAcrossNodes
    + SharedMemoryWithinNode;

HybridExecution MAY_USE {
    InterNodeMessagePassing,
    IntraNodeThreading,
    AcceleratorExecution
};

# ---------- ACCELERATED COMPUTING ----------
Accelerator MAY_BE {
    GPU,
    VectorProcessor,
    SpecialisedProcessor
};

AcceleratorExecution :=
    IdentifySuitableKernel
    -> TransferOrMapData
    -> ExecuteParallelKernel
    -> Synchronise
    -> RetrieveOrUseResult;

IF WorkloadMatchesAcceleratorArchitecture
THEN AccelerationPotential := INCREASE;

# ---------- COMPUTE NODE ----------
ComputeNode :=
    {
        ProcessorSet,
        Memory,
        AcceleratorSet,
        NetworkInterface,
        LocalStorage
    };

NodeState := {
    Available,
    Allocated,
    Busy,
    Draining,
    Failed
};

# ---------- INTERCONNECT ----------
Interconnect :=
    HighSpeedCommunicationFabric;

InterconnectProperty := {
    Bandwidth,
    Latency,
    Topology,
    Reliability
};

CommunicationCost :=
    f(
        MessageCount,
        MessageSize,
        Latency,
        Bandwidth
    );

IF CommunicationCost INCREASES
THEN ParallelEfficiency MAY_DECREASE;

# ---------- MEMORY HIERARCHY ----------
MemoryHierarchy := {
    Register,
    Cache,
    MainMemory,
    AcceleratorMemory,
    LocalStorage,
    ParallelStorage
};

DataLocality :=
    ProximityOfDataToExecutingCompute;

IF DataLocalityImproves
THEN MemoryTrafficCost MAY_DECREASE;

# ---------- HPC STORAGE ----------
HPCStorage MAY_INCLUDE {
    ParallelFileSystem,
    DistributedFileSystem,
    BurstBuffer,
    NodeLocalStorage
};

ParallelIO :=
    MultipleProcesses
    -> ConcurrentDataAccess
    -> HPCStorage;

IOPerformance :=
    f(
        DataVolume,
        AccessPattern,
        Concurrency,
        StorageBandwidth,
        MetadataCost
    );

# ---------- JOB ----------
HPCJob :=
    {
        JobIdentifier,
        Executable,
        ResourceRequest,
        InputData,
        RuntimeLimit,
        Priority,
        JobState
    };

JobState := {
    Submitted,
    Queued,
    Scheduled,
    Running,
    Completed,
    Failed,
    Cancelled
};

HPCJob ->
    Submit
    -> Queue
    -> Schedule
    -> Allocate
    -> Execute
    -> CompleteOrFail;

# ---------- SCHEDULER ----------
HPCScheduler :=
    SchedulingPolicy
    + ResourceManager;

ResourceRequest MAY_INCLUDE {
    Nodes,
    CPUs,
    GPUs,
    Memory,
    Runtime
};

Scheduler MATCHES ResourceRequest TO AvailableResources;

IF ResourcesUnavailable
THEN JobState := Queued;

# ---------- PERFORMANCE ----------
ExecutionTime :=
    EndTime - StartTime;

Throughput :=
    CompletedWork / Time;

Speedup(P) :=
    SerialExecutionTime
    / ParallelExecutionTime(P);

ParallelEfficiency(P) :=
    Speedup(P) / P;

# ---------- STRONG SCALING ----------
StrongScaling :=
    FixedProblemSize
    + IncreasingResourceCount;

IF ResourceCountIncreases
AND RuntimeDecreases
THEN StrongScalingEvidence := INCREASE;

# ---------- WEAK SCALING ----------
WeakScaling :=
    ProblemSizeIncreasesWithResourceCount;

WeakScalingEfficiency :=
    BaselineRuntime / ScaledRuntime
    WHEN WorkPerResourceApproximatelyConstant;

# ---------- AMDAHL MODEL ----------
SerialFraction := S;
ParallelFraction := 1 - S;

AmdahlSpeedup(P) :=
    1 / (S + ((1-S)/P));

IF SerialFraction > 0
THEN StrongScalingHasFiniteLimit := true;

# ---------- LOAD BALANCE ----------
LoadBalance :=
    SimilarUsefulWorkAcrossParallelResources;

LoadImbalance :=
    VariationInWorkOrWaitingAcrossResources;

IF LoadImbalanceHigh
THEN ParallelEfficiency := DECREASE;

DynamicLoadBalancing MAY_REDISTRIBUTE Work;

# ---------- SYNCHRONISATION ----------
Synchronisation :=
    CoordinateParallelExecutionAtRequiredPoints;

SynchronisationCost :=
    WaitingTime + CoordinationOverhead;

IF SynchronisationFrequencyHigh
THEN ScalingOverhead MAY_INCREASE;

# ---------- COMMUNICATION ----------
CommunicationPattern := {
    PointToPoint,
    Broadcast,
    Scatter,
    Gather,
    Reduce,
    AllReduce,
    AllToAll
};

CommunicationPattern AFFECTS {
    NetworkTraffic,
    LatencySensitivity,
    Scalability
};

# ---------- PERFORMANCE BOTTLENECK ----------
PerformanceBottleneck MAY_BE {
    ComputeBound,
    MemoryBound,
    CommunicationBound,
    IOBound,
    SynchronisationBound
};

PerformanceAnalysis ->
    Measure
    -> IdentifyBottleneck
    -> Optimise
    -> ReMeasure;

# ---------- SCIENTIFIC COMPUTING ----------
ScientificWorkload MAY_INCLUDE {
    NumericalSimulation,
    PhysicalModelling,
    ClimateSimulation,
    ComputationalFluidDynamics,
    MolecularSimulation,
    EngineeringAnalysis,
    ScientificDataProcessing
};

ScientificProblem ->
    MathematicalModel
    -> NumericalMethod
    -> ParallelAlgorithm
    -> HPCExecution
    -> Result;

# ---------- CHECKPOINTING ----------
Checkpoint :=
    SavedExecutionState;

Checkpointing MAY_SUPPORT LongRunningJobRecovery;

IF JobFails
AND ValidCheckpointExists
THEN Job MAY_RESTART_FROM Checkpoint;

# ---------- FAILURE ----------
HPCFailure := {
    ProcessFailure,
    NodeFailure,
    AcceleratorFailure,
    InterconnectFailure,
    StorageFailure,
    JobFailure
};

FailureProbability MAY_INCREASE_WITH SystemScale;

FaultTolerance MAY_USE {
    CheckpointRestart,
    Redundancy,
    TaskRecovery,
    JobResubmission
};

# ---------- ENERGY ----------
EnergyConsumption :=
    f(
        Power,
        ExecutionTime
    );

EnergyEfficiency :=
    UsefulComputation / EnergyConsumption;

PerformancePerWatt :=
    ComputationalPerformance / Power;

# ---------- HPC ARCHITECTURE ----------
HPCArchitecture := {
    Supercomputer,
    HPCCluster,
    GPUCluster,
    MassivelyParallelSystem,
    HeterogeneousHPCSystem
};

Supercomputer IS_A HPCSystem;
HPCCluster IS_A HPCSystem;
HeterogeneousHPCSystem CONTAINS MultipleComputeArchitectures;

# ---------- SEMANTIC REASONING ----------
IF WorkloadHasHighComputationalDemand
AND WorkloadExposesParallelism
THEN HPCSuitability := HIGH;

IF CommunicationTime > ComputationTime
THEN CommunicationBottleneck := likely;

IF MemoryBandwidthSaturated
THEN Workload MAY_BE MemoryBound;

IF AcceleratorUtilisationLow
THEN AcceleratorEfficiency := review;

IF SerialFractionIncreases
THEN MaximumSpeedup := decrease;

IF ResourceCountIncreasesWithoutUsefulSpeedup
THEN ScalingEfficiency := decrease;

IF LoadBalanced
AND CommunicationOverheadLow
THEN ParallelEfficiencyPotential := increase;

# ---------- SEMANTIC EQUIVALENCE ----------
SEMANTIC_EQUIVALENCE {
    "high performance computing" ~= "HPC";
    "supercomputing" ~= "high performance computing" WHEN context == large_scale_computation;
    "parallel application" ~= "parallel program";
    "compute node" ~= "HPC node" WHEN context == high_performance_computing;
    "parallel efficiency" ~= "parallel scaling efficiency";
    "strong scaling" ~= "fixed-problem scaling";
    "weak scaling" ~= "scaled-problem scaling";
    "accelerated computing" ~= "hardware-accelerated computing";
}

# ---------- QUERY FLOW ----------
QUERY(q) ->
    NORMALISE(q) ->
    IDENTIFY_HPC_SYSTEM(q) ->
    IDENTIFY_WORKLOAD(q) ->
    IDENTIFY_EXECUTION_MODEL(q) ->
    IDENTIFY_RESOURCES(q) ->
    IDENTIFY_COMMUNICATION(q) ->
    IDENTIFY_MEMORY_MODEL(q) ->
    IDENTIFY_PERFORMANCE_METRICS(q) ->
    IDENTIFY_SCALING(q) ->
    MAP_TO_ONTOLOGY(q) ->
    APPLY_REASONING(q) ->
    SCORE_CONFIDENCE(q) ->
    RETURN_RELEVANT_KNOWLEDGE;

# ---------- CONFIDENCE ----------
CONFIDENCE(assertion) :=
    weighted_mean(
        source_reliability,
        computational_consistency,
        performance_consistency,
        semantic_consistency,
        evidence_strength,
        contextual_relevance
    );

CONFIDENCE >= 0.90 => HIGH;
CONFIDENCE >= 0.70 AND < 0.90 => MODERATE;
CONFIDENCE < 0.70 => REVIEW;

# ---------- AI CONSUMPTION ----------
AI_CONSUMPTION {
    primary_entity: HighPerformanceComputing;
    ontology: HighPerformanceComputingOntology;
    parse_entities: true;
    parse_relationships: true;
    preserve_relationship_direction: true;
    resolve_semantic_equivalence: true;
    execute_reasoning_rules: true;
    apply_confidence_weighting: true;
    preserve_provenance: true;
}

# ---------- UPDATE MODEL ----------
UPDATE_MODEL {
    new_resource:
        validate
        -> classify
        -> identify_performance_properties
        -> relate
        -> integrate;

    new_workload:
        classify
        -> identify_parallelism
        -> identify_resource_requirements
        -> identify_communication_pattern
        -> integrate;

    new_execution_model:
        validate
        -> identify_memory_model
        -> identify_process_thread_relationships
        -> identify_synchronisation
        -> integrate;

    performance_revision:
        update_measurements
        -> identify_bottleneck
        -> recompute_speedup
        -> recompute_efficiency
        -> regenerate_flow_hash;

    conflict:
        preserve_provenance
        -> compare_evidence
        -> adjust_confidence
        -> flag_review;
}

# ---------- FLOW HASH ----------
FLOW_HASH_INPUT :=
    DOMAIN
    + SUBDOMAIN
    + ONTOLOGY
    + HPCSystem
    + HPCResource
    + HPCWorkload
    + ExecutionModel
    + Parallelism
    + HPCArchitecture
    + RELATIONSHIPS
    + SEMANTIC_REASONING
    + SEMANTIC_EQUIVALENCE
    + UPDATE_MODEL;

FLOW_HASH :=
    HASH(FLOW_HASH_INPUT);

# ---------- TERMINAL ----------
HighPerformanceComputingOntology
    -> IdentifyComputationalProblem
    -> DetermineParallelStructure
    -> SelectExecutionModel
    -> SelectHPCArchitecture
    -> AllocateResources
    -> MapParallelWork
    -> ExecuteComputation
    -> CommunicateAndSynchronise
    -> MeasurePerformance
    -> AnalyseScaling
    -> IdentifyBottlenecks
    -> OptimiseResourceUse
    -> ProduceComputationalResult;

END FLOW